Conventionally, techniques of separating time series signals have been studied, with a focus on sound source separation for separating, for each sound source, acoustic signals such as voice coming from a plurality of sound sources and observed by a plurality of microphones. Among the techniques, a method that uses independent component analysis has been actively studied as a technique for so-called blind sound source separation which needs no prior information such as sound source directions.
Signal separation according to the independent component analysis is a technique of separating signals for each signal source under the assumption that acoustic signals coming from the signal sources are mutually statistically independent. The independent component analysis may be formulated as an optimization problem for obtaining parameters of a demixing matrix used for separation of signals based on a criterion for maximizing statistical independence of signals separated by the demixing matrix. However, the solution is not analytically obtained, and the demixing matrix parameters have to be repeatedly updated for a sequential optimization method such as a gradient method. Thus, there is a problem that the amount of calculation for obtaining sufficient signal separation accuracy is increased. Also, to obtain a solution with high accuracy and with a small amount of calculation, a parameter called step size that is used in repetitive calculation has to be appropriately adjusted in advance by hand or by an observation signal.
On the other hand, there is proposed an auxiliary function method which achieves, by using an auxiliary function set under a certain condition for an objective function of the optimization problem, stable separation accuracy with a smaller amount of calculation compared to a natural gradient method while requiring no parameter setting such as the step size. Also, an auxiliary function method is being proposed of performing independent vector analysis which does not require post-processing called permutation, which is necessary in sound source separation by the independent component analysis.
However, with the conventional techniques, it is not possible to perform the blind sound source separation process in real time while coping with changes in the environment such as movement or emergence of a sound source.